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SUMMARY.md
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# Summary
* [前言](README.md)
* [第一部分 数学基础](math/math.md)
* [第一章 数学分析](math/analytic/introduction.md)
* [常用函数](math/analytic/common-function.md)
* [牛顿法](math/analytic/niudun.md)
* [梯度下降](math/analytic/gradient_descent.md)
* [最小二乘](math/analytic/least-square.md)
* [拉格朗日乘子法（Lagrange Multiplier\) 和KKT条件](math/analytic/lagelangri-kkt.md)
* [凸优化](math/analytic/tuyouhua.md)
* [数值计算](math/analytic/shu-zhi-ji-suan.md)
* [第二章 概率论和统计](math/probability.md)
* [统计学习方法概论](math/probability/prob-methodology.md)
* [最大似然估计](math/probability/mle.md)
* [蒙特卡罗方法](math/probability/mcmc1.md)
* [马尔科夫链](math/probability/markov-chain.md)
* [MCMC采样和M-H采样](math/probability/mcmc-mh.md)
* [Gibbs采样](math/probability/gibbs.md)
* [第三章 线性代数和矩阵](math/linear-matrix/linear-matrix.md)
* [LAPACK](math/linear-matrix/lapack.md)
* [特征值与特征向量](math/linear-matrix/tezhengzhihetezhengxiangliang.md)
* [第二部分 机器学习](ml/ml.md)
* [第四章 机器学习基础](ml/pythonml.md)
* [机器学习库（numpy,scikit,scipy）](ml/pythonml/ji-qi-xue-xi-ku.md)
* [机器学习库（Pandas,pySpark）](ml/pythonml/ji-qi-xue-xi-ku2.md)
* [机器学习库（tensorflow, keras）](ml/pythonml/ji-qi-xue-xi-ku3.md)
* [机器学习库（scipy,matplotlib）](ml/pythonml/ji-qi-xue-xi-ku-ff08-scipy-matplotlib.md)
* [模型度量](ml/pythonml/ml-metrics.md)
* [交叉验证](math/analytic/cross-validation.md)
* [生成模型和判别模型](ml/pythonml/gen-descri.md)
* [机器学习中的距离](ml/pythonml/distance.md)
* [机器翻译模型度量](ml/pythonml/ji-qi-fan-yi-mo-xing-du-liang.md)
* [第六课：数据清洗和特征选择](ml/clean-feature/cleanup-feature.md)
* [PCA](ml/clean-feature/pca.md)
* [ICA](ml/clean-feature/ica.md)
* [One-hot编码](ml/clean-feature/one-hot.md)
* [scikit-learn PCA](ml/clean-feature/scikit-pca.md)
* [线性判别分析LDA](ml/clean-feature/xian-xing-pan-bie-fen-xi-lda.md)
* [用scikit-learn进行LDA降维](ml/clean-feature/scikit-lda.md)
* [奇异值分解\(SVD\)原理与在降维中的应用](ml/clean-feature/svd.md)
* [局部线性嵌入\(LLE\)原理](ml/clean-feature/lle.md)
* [scikit-learn LLE](ml/clean-feature/scikit-lle.md)
* [spark特征选择](ml/clean-feature/spark-fselect.md)
* [Spark特征提取](ml/clean-feature/spark-fextract.md)
* [异常数据监测](ml/clean-feature/outlier-detect.md)
* [数据预处理](ml/clean-feature/datapreprocess.md)
* [特征工程](ml/clean-feature/te-zheng-gong-cheng.md)
* [第七课： 回归](ml/regression/regression.md)
* [1. 线性回归](ml/regression/linear-regression.md)
* [10.最大熵模型](ml/regression/max-entropy.md)
* [11.K-L散度](ml/regression/kl.md)
* [坐标下降和最小角](ml/regression/cordinate-angle.md)
* [线性回归小结](ml/regression/linear-regression-summary.md)
* [Logistic回归](ml/regression/logistic.md)
* [Logistic回归小结](ml/regression/logistichui-gui-xiao-jie.md)
* [SoftMax回归](ml/regression/softmax.md)
* [第九课：决策树](ml/decisiontree.md)
* [ID3](ml/decisiontree/id3.md)
* [C4.5](ml/decisiontree/c45.md)
* [CART](ml/decisiontree/cart.md)
* [总结](ml/decisiontree/summary.md)
* [实现代码](ml/decisiontree/code.md)
* [第十三课：SVM](ml/svm.md)
* [感知机模型](ml/svm/gan-zhi-ji-mo-xing.md)
* [线性SVM](ml/svm/linear-svm.md)
* [软间隔最大化模型](ml/svm/soft-margin-max.md)
* [核函数](ml/svm/kernel-method.md)
* [SMO算法原理](ml/svm/smo.md)
* [SVM回归](ml/svm/svm-regression.md)
* [scikit-learn SVM](ml/svm/scikit-learn-svm.md)
* [支持向量机高斯核调参](ml/svm/gaosi-kernel.md)
* [SVM代码实现](ml/svm/svm-code.md)
* [集成学习](ml/integrate.md)
* [Adaboost原理](ml/integrate/adaboost.md)
* [scikit-learn Adaboost](ml/integrate/scikit-learn-adaboost.md)
* [梯度提升树（GBDT）](ml/integrate/gbdt.md)
* [scikit GBDT](ml/integrate/scikit-gbdt.md)
* [Bagging与随机森林](ml/integrate/random-forest.md)
* [XGBOOST](ml/integrate/xgboost.md)
* [scikit-learn 随机森林](ml/integrate/scikit-learn-rf.md)
* [第十五课：聚类](ml/cluster.md)
* [K-Mean](ml/cluster/kmeans.md)
* [KNN](ml/cluster/KNN.md)
* [scikit-learn KNN](ml/cluster/knnshi-jian.md)
* [KNN 代码](ml/cluster/knn-code.md)
* [scikit-learn K-Means](ml/cluster/scikit-k-means.md)
* [BIRCH聚类算法原理](ml/cluster/birch.md)
* [scikit-learn BIRCH](ml/cluster/scikit-learn-birch.md)
* [DBSCAN密度聚类算法](ml/cluster/dbscan.md)
* [scikit-learn DBSCAN](ml/cluster/scikit-learn-dbscan.md)
* [谱聚类（spectral clustering）原理](ml/cluster/spectral.md)
* [scikit-learn 谱聚类](ml/cluster/scikit-spectral.md)
* [近邻传播算法](ml/cluster/ap.md)
* [混合高斯模型](ml/cluster/gmm.md)
* [关联分析](ml/associative/associative.md)
* [典型关联分析\(CCA\)原理](ml/associative/cca.md)
* [Apriori算法原理](ml/associative/apriori.md)
* [FP Tree算法原理](ml/associative/fptree.md)
* [PrefixSpan算法原理](ml/associative/prefixspan.md)
* [Spark FP Tree算法和PrefixSpan算法](ml/associative/spark-fptree-prefixspan.md)
* [推荐算法](ml/recommand/recommand.md)
* [矩阵分解协同过滤推荐算法](ml/recommand/matrix-filter.md)
* [SimRank协同过滤推荐算法](ml/recommand/simrank.md)
* [Spark矩阵分解推荐算法](ml/recomand/spark-factor.md)
* [分解机\(Factorization Machines\)推荐算法原理](ml/recommand/fm.md)
* [美团推荐算法](ml/recommand/meituan.md)
* [MapReduce ItemCF](ml/recommand/mr-itemcf.md)
* [基于标签的用户推荐系统](ml/recommand/label-recommand.md)
* [10种传统机器学习算法，阿里工程师总结](ml/recommand/10zhong-chuan-tong-ji-qi-xue-xi-suan-fa-ff0c-a-li-gong-cheng-shi-zong-jie.md)
* [第十七课：EM算法](ml/em/em.md)
* [第十九课：贝叶斯网络](ml/bayes.md)
* [朴素贝叶斯](ml/bayes/po-su-bei-xie-si.md)
* [scikit-learn朴素贝叶斯](ml/bayes/scikit-simple-bayes.md)
* [朴素贝叶斯实际应用](ml/bayes/simple-bayes-real-use.md)
* [朴素贝叶斯代码](ml/bayes/simple-bayes-code.md)
* [变分贝叶斯推断](ml/bayes/bian-fen-bei-xie-si-tui-duan.md)
* [第二十一课：LDA主题模型](ml/lda/lda.md)
* [第二十三课：隐马尔科夫模型HMM](ml/hmm/hmm.md)
* [HMM前向后向算法评估观察序列概率](ml/hmm/hmm-forward-backward.md)
* [鲍姆-韦尔奇算法求解HMM参数](ml/hmm/bmwl-hmm.md)
* [维特比算法解码隐藏状态序列](ml/hmm/viterb-hmm.md)
* [用hmmlearn学习隐马尔科夫模型HMM](ml/hmm/hmmlearn.md)
* [马尔科夫蒙特卡洛](ml/hmm/markv-mengtekluo.md)
* [条件随机场CRF](ml/crf/crf.md)
* [从随机场到线性链条件随机场](ml/crf/linear-crf.md)
* [前向后向算法评估标记序列概率](ml/crf/back-forth.md)
* [维特比算法解码](ml/crf/crf-viterbi.md)
* [第三部分 深度学习](dl/dl.md)
* [深度学习层](dl/layers/layers.md)
* [核心层](dl/layers/core.md)
* [卷积层](dl/layers/conv.md)
* [池化层](dl/layers/pooling.md)
* [局部连接层](dl/layers/lcnn.md)
* [循环层](dl/layers/rnn.md)
* [嵌入层](dl/layers/ebbedded.md)
* [合并层](dl/layers/merge.md)
* [高级激活层](dl/layers/activation.md)
* [归一化层](dl/layers/regular.md)
* [噪声层](dl/layers/nosie.md)
* [层包裹](dl/layers/wrapper.md)
* [自定义层](dl/layers/userdefine.md)
* [第二十五课：深度学习](dl/introduction/introduction.md)
* [基本概念](dl/introduction/ji-ben-gai-nian.md)
* [深度神经网络（DNN）模型与前向传播算法](dl/introduction/dnn-fp.md)
* [深度神经网络（DNN）反向传播算法\(BP\)](dl/introduction/dnn-bp.md)
* [反向传播](dl/introduction/back-propagation.md)
* [反向传播2](dl/introduction/READ.md)
* [DNN损失函数和激活函数的选择](dl/introduction/dnn-loss.md)
* [深度神经网络（DNN）的正则化](dl/introduction/dnn-normal.md)
* [参考文献](dl/reference.md)
* [第二十六课 卷积神 经网络\(Convolutional Neural Netowrk\)](dl/cnn/introduction.md)
* [卷积神经网络\(CNN\)模型结构](dl/cnn/cnn-arch.md)
* [卷积神经网络\(CNN\)前向传播算法](dl/cnn/cnn-fp.md)
* [卷积神经网络\(CNN\)反向传播算法](dl/cnn/cnn-bp.md)
* [对抗生成网络\(Generative Adversarial Networks\)](dl/gan/gan.md)
* [GAN原理](ml/gan/gan-principle.md)
* [InfoGAN](dl/gan/infogan.md)
* [DCGAN](dl/gan/dcgan.md)
* [VAE](dl/gan/vae.md)
* [受限波尔兹曼机](dl/rbm/rbm.md)
* [RBM code](dl/rbm/rbm-code.md)
* [DBN](dl/rbm/dbn.md)
* [RBM原理](dl/rbm/rbm-yuanli.md)
* [RNN](dl/rnn/rnn.md)
* [Bidirectional RNNs](dl/rnn/bidirectional-rnns.md)
* [Deep \(Bidirectional\) RNNs](dl/rnn/deep-bidirectional-rnns.md)
* [LSTM模型与前向反向传播算法](dl/rnn/lstm.md)
* [随时间反向传播（BPTT）算法](dl/rnn/bptt.md)
* [循环神经网络\(RNN\)模型与前向反向传播算法](dl/rnn/rnn-bptt.md)
* [自动编码器](dl/encoder/encoder.md)
* [堆叠降噪自动编码器](dl/encoder/stack-denoise-encoder.md)
* [降噪自动编码器](dl/encoder/denoise-encoder.md)
* [sparse自动编码器](dl/encoder/sparse-autoencoder.md)
* [Keras自动编码器](dl/encoder/keras-autoencoder.md)
* [word2vec](dl/word2vec/word2vec.md)
* [CBOW与Skip-Gram模型基础](dl/word2vec/cbow-skip-n.md)
* [基于Hierarchical Softmax的模型](dl/word2vec/hierarc-softmax.md)
* [基于Negative Sampling的模型](dl/word2vec/negative-sampling.md)
* [强化学习](dl/reinforcement/reinforcement.md)
* [Q-Learning](dl/reinforcement/q-learning.md)
* [梯度下降策略优化推导](dl/reinforcement/ti-du-xia-jiang-ce-lve-you-hua-tui-dao.md)
* [策略网络](dl/reinforcement/policy-network.md)
* [bandit算法](dl/reinforcement/banditsuan-fa.md)
* [蒙特卡洛树搜索](dl/reinforcement/meng-te-qia-luo-shu-sou-suo.md)
* [多臂赌博机\(Multi-arm Bandits\)](dl/reinforcement/multi-bandit.md)
* [马尔可夫决策过程MDP](dl/reinforcement/mdp.md)
* [动态编程](dl/reinforcement/dynamic-programming.md)
* [蒙特卡洛方法](dl/reinforcement/monte-carlo.md)
* [时序差分学习](dl/reinforcement/shi-xu-cha-fen-xue-xi.md)
* [A3C算法](dl/reinforcement/a3csuan-fa.md)
* [Multi-steps bootstraping](dl/reinforcement/multi-steps-bootstraping.md)
* [Planning and Learning with Tabular Methods](dl/reinforcement/tabular-method.md)
* [DQN](dl/reinforcement/dqn.md)
* [Policy Gridient](dl/reinforcement/policy-gridient.md)
* [Actor Critic](dl/reinforcement/actor-critic.md)
* [DDPG \(Deep Deterministic Policy Gradient\)](dl/reinforcement/ddpg.md)
* [PPO\(Proximal Policy Optimization \)](dl/reinforcement/ppo.md)
* [Alpha-Beta剪枝算法详解](dl/reinforcement/alpha-beta.md)
* [进化算法](ml/evolution/evolution.md)
* [遗传算法](ml/evolution/yichuansuanfa.md)
* [进化策略](ml/evolution/evolution-strategy.md)
* [NEAT](ml/evolution/neat.md)
* [自然语言处理](nlp/nlp.md)
* [文本挖掘的分词原理](nlp/text-mine.md)
* [HashTrick](nlp/hashtrick.md)
* [TF-IDF](nlp/tf-idf.md)
* [中文文本挖掘预处理](nlp/preprocessing.md)
* [英文文本挖掘预处理](nlp/english-preprocess.md)
* [潜在语义索引\(LSI\)](nlp/lda/lsi.md)
* [非负矩阵分解\(NMF\)](nlp/lda/nmf.md)
* [LDA基础](nlp/lda/lda.md)
* [LDA求解之Gibbs采样算法](nlp/lda/lda-gibbs.md)
* [LDA求解之变分推断EM算法](nlp/lda/vi-em.md)
* [scikit-learn LDA主题模型](nlp/lda/scikit-learn-lda.md)
* [语音识别](voice/introduction.md)
* [GMM-HMM](voice/gmm-hmm.md)
* [目录](voice/voicemulumd.md)
* [第四部分 学习资源](resources/introduction.md)
* [机器学习](resources/ml/list.md)
* [强化学习](resources/rl/list.md)
* [自然语言处理](resources/nlp/list.md)
* [深度学习](resources/dl/list.md)
* [流行网络结构](liu-xing-wang-luo-jie-gou.md)
* [mobilenet](dl/popnet/mobilenet.md)
* [ResNet](dl/popnet/resnet.md)
* 并行学习
* [美团并行学习实践](dl/paralleldl/mei-tuan-bing-xing-xue-xi-shi-jian.md)
* AI应用
* [美团外卖AI技术](dl/dlapp/mei-tuan-wai-mai-ai-ji-zhu.md)
* [美团推荐排序](dl/dlapp/mei-tuan-tui-jian-pai-xu.md)
* [AutoML在推荐系统应用](dl/dlapp/automlzai-tui-jian-xi-tong-ying-yong.md)
* 参考
* [AI领域国内创业公司](ailing-yu-guo-nei-chuang-ye-gong-si.md)